CN101127657A - Dynamic modeling and control technology for independent mobile sensor network - Google Patents

Abstract

The utility model relates to a dynamic modeling and control technique of a self-moving sensor network and belongs to the sensor network technology. According to the technical proposal provided by the utility model, the dynamic modeling and controlling technology of a self-moving sensor network comprises the steps: the step I: build a distributed moving sensor network model based on the local Delaunay triangulation and other applicable diagram; the step II: use two methods to perform the distributed self-arrangement and the self-organization in the network for the sensor controlling node of each region in the distributed moving sensor network model; the step III: use the formation algorithm to control the network node formation and the node cooperation to achieve the purpose of fully energy-saving; the step IV: build the ad-hoc route data transmission protocol of the self-organization network of the distributed mobile robots to realize the stable work of the entire self-moving sensor network. The utility model can provide an algorithm for the repeatable configuration, cooperation, route development and verification of the sensor network with a plurality of moving sensor nodes.

Description

Independent mobile sensor network dynamic modeling and control technology

Technical field

The present invention relates to sensor network technique, specifically a kind of independent mobile sensor network dynamic modeling and control technology.

Background technology

Mobile sensor network military and civil aspect can both obtain a large amount of application, such as work, environmental monitoring, target following and remote sensing under battlefield supervision, search and defence, the hazardous environment.And various parameter monitorings such as the temperature in the industrial production, pressure, flow, the monitoring of paper mill haystack, the monitoring of grain depot monitoring and herding industry etc.Its achievement will significantly improve enterprise's production informationization and automatic management level, have broad application prospects.

In the recent period, the research of distributed wireless sensing network has caused people's attention as an important field.The example of wireless sensor network comprises: Berkeley motes and wireless integrated network transducer.The application and the research topic of different sensor networks also occur in a large number.

Sensor network and traditional ad-hoc wireless network are being owned many challenges together.Yet the communication mode of sensor network is different with the communication mode of other networks IP form.The function of sensor network provides the user with observation information.Its infrastructure and individual sensor node are fully transparent to the user.And in network, the energy that can offer transducer is limited.Communication can expend more energy than monitoring and data processing.For wireless sensor network, one of challenge is the exploitation of locality algorithm, it should be upgradeable, robust with energy-conservation.Consider these constraints of energy-conservation, fault-tolerance, upgradability and task adaptivity, a large amount of scholars has proposed a lot of communication structures and algorithm.For the microsensor network, Directed Diffusion and LEACH are two kinds of typical data communication protocols.Although the focus of this invention is not the network configuration of mobile sensor network, but the prototype of network but can provide the platform of a repeated configuration for the test of heterogeneous networks structure and algorithm.

In radio sensing network, the geometry of each sensor node location should at first be determined, so that provide Useful Information for spatially distributed signal processing.And in the mobile network, the positional information with communication range can make how much routing algorithm transmission information more effective.About the research of this theme comprises physical positioning method and location estimation method.The most general method that is used for outdoor a kind of definite node location is with Global Positioning Systems (GPS).Yet the energy that GPS consumes is a lot, and can not be applied to indoor.In addition, a lot of local positioning systems are set up, and are used for supporting indoor positioning and location estimation.At present, most of existing systems are based on all that fixing infrastructure locatees.But for distributed sensor networks, infrastructure generally is freely, so these prior aries can not directly apply to large-scale sensor network.Therefore, the location estimation of radio sensing network has also attracted many researchers' attention.Corresponding with local positioning system, the location technology that is used for robot comprises dead reckoning and triangulation demarcation.Relevant estimation and sensor fusion techniques comprise KalmanFilter, Markov localization and based on the Monte Carlolocalization of Bayesian inference network.Based on location and location estimation method, a large amount of is suggested with network associated location algorithm and signal processing method.In this invention, a kind of distributed model based on locating information and graph theory has been proposed.

It is generally acknowledged a plurality of robots than a robot faster, have bigger potentiality on finishing the work more efficiently.As an important field of research, make multirobot cooperation and formation control method emerge in multitude.The machine control structure of multirobot cooperation is as subsumption architecture, reaction control with mix control, be suggested based on the control of behavior.Can coordinate a large amount of robots based on the Robotics of behavior finishes target and keeps the result.For the mankind, very common cooperation behavior with perception and symbolic communication ability has also caused the attention with the relevant researcher of Multi-Agent Cooperation.The pheromones robot utilizes the pheromones phenomenon to coordinate the behavior of a large amount of robots.Some previous researchs mainly are about combined task planning, based on the sensing and the control of the robot system of event-driven method.Prior art remains few about the conclusion of whole radio sensing network Mathematical Modeling, though these conclusions and technology have separately advantage for the analysis of distributed system and design, the mathematical theory that lacks system instructs.

Summary of the invention

The objective of the invention is to design a kind of independent mobile sensor network dynamic modeling and control technology, but for repeated configuration, cooperation, the route exploitation of sensor network with a large amount of movable sensor nodes with verify a kind of algorithm.The present invention makes full use of a kind of orientation or non-directional figure, can set up mobile robot's wireless network model, and simultaneously, graph theory also is used in the network coverage and sensor configuration of sensor network, and advantage is more obvious.

According to technical scheme provided by the invention, independent mobile sensor network dynamic modeling and control technology comprise the steps:

Step 1: set up distributed mobile sensor network model based on local Di Langnei triangulation and Wei Lang noy figure, wherein the Di Langnei triangulation is described the transducer annexation with the triangle geometry, tie up the monitoring range that bright noy figure is used for describing sensor node, on the model basis that this combines, set up the static and dynamic performance of expressing the whole sensor network;

Step 2: utilize two kinds of methods to carry out the distributed from disposing and self-organizing of network to the control sensor node under arbitrary zone in this distributed mobile sensor network model, these two kinds of methods are respectively: based on the distributed self-organizing method of virtual potential field method and Di Langnei triangulation; Distributed self-organizing method based on the modified model particle group optimizing method;

Step 3: adopt formation algorithm controls network node to form into columns and the node cooperation, reach abundant purpose of energy saving;

Step 4: set up distributed mobile robot's self-organizing network ad-hoc route data host-host protocol, realize the steady operation of whole independent mobile sensor network.

After step 1, before the step 2, carry out the basic exercise credit based on this model and analyse;

When analyzing, set up the dynamic model of each mobile robot's subsystem respectively according to each mobile robot's position, deflection, speed and controlled quentity controlled variable information; According to the dynamic model of each subsystem of setting up and the relation between integrated subsystem and the adjacent node, set up distributed model again based on graph theory; Then, based on the distributed model of being built, carry out information gathering, fusion.

Based on virtual potential field method and the distributed self-organizing method Di Langnei triangulation, make transducer carry out self-organizing in no constraint space;

The basic thought of virtual potential field method is: the motion of sensor node in environment is considered as a kind of virtual motion that manually is subjected in the field of force; Barrier produces repulsion to robot, and impact point produces gravitation, the making a concerted effort as the accelerative force of sensor node, the movement velocity of Control Node and direction of repulsion and gravitation;

When self-organizing, derive its control law by the performance index function of a robot; When considering whole multi-robot system, derive the group control rule; Foundation is based on the self-organizing method of no constraint space in the distributed self-organizing method of virtual potential field method and Di Langnei triangulation.

On the basis of the no constraint space self-organizing of transducer, add the motion planning of the biological excitation of simulation;

The basic thought of the motion planning of the biological excitation of simulation is: the simulated nervous system behavior, virtual potential field power is limited in the bounded interval, and make the comparatively continuous variation of virtual potential field power, and then make that the movement locus of sensor node is comparatively level and smooth.

Distributed self-organizing method based on the modified model particle group optimizing method mainly is to utilize particle cluster algorithm, and according to the positional information of known sensor node, calculates the follow-up location of other node; The sensor node of network is divided into main bunch head, auxilliary bunch head and ordinary node, and wherein, a main bunch head plays a part to drive and instruct whole network node, and auxilliary bunch head plays a part to drive and instruct the node in certain zone, and remaining node is an ordinary node; By the drive and the directive function of main and auxiliary bunch of head, carry out the distributed of network from disposing and self-organizing; Arrive assigned address by main and auxiliary bunch of headband neck network, and guide network to launch gradually.

When under barrier and environmental constraints are arranged, carrying out the self-organizing of transducer, derive its control law according to the energy function of the system that barrier is arranged; Foundation is based on the transducer self-organizing method under barrier and environmental constraints in the distributed self-organizing method of virtual potential field method and Di Langnei triangulation.

With the distributed self-organizing method of transducer be applied in the dispersion that comprises node, converge with fault-tolerant task in, solve and disperse and converge problem by adjusting desired distance between two nodes.

Distributed self-organizing method is used to solve the tracking Control problem of the movable sensor with non-holonomic constraint.

Described formation algorithm comprises asynchronous formation algorithm, form into columns algorithm and composite formation's algorithm synchronously; Constituting by synchronous formation algorithm and asynchronous formation algorithm wherein by composite formation's algorithm.

Di Langnei triangulation and locating information also are helpful to the route communication.Existing routing method does not have the knowledge of actual physics distance and bearing aspect between information source and the information destination.The node that provides only knows that its logic connects.Form into columns when handling upgradeable, huge dynamic robot, as: when the autonomous aircraft of fast moving was formed into columns, locating information was very useful.Each robot relative position may change very soon, simultaneously, in order to keep forming into columns, communication is essential reliably.To such an extent as to the path that the existing routing method communication delay finds too greatly is unreliable under this class situation.From sensor network kinematics and distributed Di Lang inner model aspect, it is relatively easy finding out the adjacent node that need not to exchange a lot of control informations.Path distance is limited by the distance between source and destination.Compare with existing Routing Protocol, this allows new path to be set up fast.

Description of drawings

Fig. 1 (a) is the mobile sensor network schematic diagram.

Fig. 1 (b) is the local coordinate system of two robots.

Fig. 2 (a) is distributed sensor networks model Delaunay triangulation and Voronoi figure.

Fig. 2 (b) is R in the distributed sensor networks model ₂One jump the adjacent node schematic diagram.

Fig. 2 (c) is the adjacent single-hop sensor coordinates relation in the distributed sensor networks model.

The information fusion and the transmission of Fig. 3 (a) distributed sensor networks.

Fig. 3 (b) is the three dimensions expansion of distributed sensor networks.

Fig. 4 is the node region coverage diagram.

Fig. 5 is an improved Di Langnei triangulation under the constraint environment.

Circle representative sensor node among the figure, the connection between the straight line representation node.

Embodiment

The main contents that the present invention relates to comprise following 5 points: 1) based on the foundation of the distributed model of local Di Langnei triangulation; 2) sensing network kinematics; 3) distributed self-organizing; 4) composite formation and cooperation algorithm; 5) distributed mobile robot ad-hoc route and Data Transport Protocol.

Its purpose may be summarized to be following 5 points: 1) based on the distributed model of local Di Langnei triangulation, set up the method for sensor network being carried out general analysis; 2), make that the mobile robot can be constantly near desired locations to the kinematic research of sensor network; 3) distributed self organization ability can be so that network disperses, converges, oneself's identification and maximization monitoring range; 4) a kind of composite formation of exploitation and cooperation algorithm can make multirobot cooperate mutually so that reach common target; 5) distributed mobile robot ad-hoc route of exploitation and Data Transport Protocol.

1, comprises the mobile apparatus human model based on the distributed mobile sensor network model mobile sensor network model of local Di Langnei triangulation and based on the distributed model of graph theory.In mobile sensor network, movable sensor is wireless connections, shown in Fig. 1 (a).Be expressed as R={R ₁, R ₂..., R _nN robot be configured in the smooth zone.R _iThe parameter that is provided with of individual robot is q _i(t)=[x _i, y _i, θ _i] ^T, i=1,2 ..., n.X wherein _iAnd y _iBe R _iCoordinate in the individual robot place local coordinate system, θ _iIt is its deflection.Shown in Fig. 1 (b).Its dynamic model is described as ${\stackrel{.}{q}}_i=f_i(q_i,u_i),$ Wherein, u _iBe subsystem R _iControl input.

In this invention, relate to complete model and non-complete model based on the mobile robot.Complete model is described below:

${\stackrel{.}{x}}_i=v_{\mathrm{ix}},{\stackrel{.}{y}}_i=v_{\mathrm{iy}},{\stackrel{.}{v}}_{\mathrm{ix}}=u_{i1},{\stackrel{.}{v}}_{\mathrm{iy}}=u_{i2}---\left(1\right)$

Wherein, v _IxAnd v _IyBe in local coordinate system respectively along the translational speed of x and y direction, the control input u in the model _iBe defined as: u _i={ u _I1, u _I2} ^T

Suppose that translational speed and rotary speed all are controlled, the robot model fi with nonholonomic constraint can be described to:

${\stackrel{.}{x}}_i=v_i\·\cos {\mathrm{\θ}}_i,{\stackrel{.}{y}}_i=v_i\·\sin {\mathrm{\θ}}_i,{\stackrel{.}{\mathrm{\θ}}}_i={\mathrm{\ω}}_i,{\stackrel{.}{v}}_i=u_{i1},{\stackrel{.}{\mathrm{\ω}}}_i=u_{i2}---\left(2\right)$

Wherein, v _iBe mobile robot's radial velocity, ω _iBe angular speed.Mobile robot's nonholonomic constraint is described as :-x _iCos θ _i+ y _iSin θ _i=0.In local coordinate system, parameter is set in definition and control variables is unnecessary.For example: shown in Fig. 1 (b), at ∑ _iIn defined q _i

In the distributed model based on graph theory, but mobile sensor network is a system with setting parameter and controlled variable, but its setting parameter and controlled variable are expressed as q={q respectively ₁, q ₂..., q _n} ^TAnd u={u ₁, u ₂..., u _n} ^TMulti-robot system can be modeled as a multidimensional interconnected systems, and whole system is expressed as: $\stackrel{.}{q}=f(q,u)$ , wherein f is the vector field of system dynamic model.In this invention, the distributed model based on graph theory has been proposed.It is made up of two parts: the relation between subsystem and the adjacent node 1) dynamic model 2 of each subsystem).

The definition of multi-robot system global object for convenience, ${\tilde{p}}_i={\{{\tilde{x}}_i\left(t\right),{\tilde{y}}_i\left(t\right)\}}^T$ Be described as the R of robot in the unified rest frame _iThe position, $\tilde{p}={\{{\tilde{p}}_1,{\tilde{p}}_2,\·\·\·,{\tilde{p}}_n\}}^T$ Be described as the position of all robots in the unified rest frame.And definition p _i={ x _i(t), y _i(t) } ^TBe the local coordinate system ∑ _iThe middle R of robot _iThe position.

Relation between adjacent machines people's node defines by Di Langnei triangulation and Wei Lang noy figure, as Fig. 2 (a).In smooth zone, tieing up bright noy figure and Di Langnei triangulation all is dual concerning the other side.Tie up bright noy figure and defined the overlay area of each movable sensor, the Di Langnei triangulation has then defined adjacent node on how much of the movable sensors and the relation between them.

The overlay area of sensor network is an important attribute to network.In this invention, tie up bright noy figure and be used for describing this attribute, shown in the dotted line among Fig. 2 (a).Consider in parameter ${\tilde{p}}_i={\{{\tilde{x}}_i\left(t\right),{\tilde{y}}_i\left(t\right)\}}^T$ The colony of the robot of following configuration, each robot all should cover the part among the overall area Ω.In Ω, sensor node R _iThe subregion that is covered is defined as:

$V_i=\{x\&Element;\mathrm{\&Omega;}|\left|\right|x-{\tilde{p}}_i\left|\right|<\left|\right|x-{\tilde{p}}_j\left|\right|\}\&ForAll;j=1,\&CenterDot;\&CenterDot;\&CenterDot;,n,j\&NotEqual;i---\left(3\right)$

V={V ₁, V ₂..., V _nForm a kind of division of flat site Ω, wherein sensor node R _iOnly at subregion V _iThe generation effect.The R of robot _iBe also referred to as figure " generator ".V _iBe defined by the convex polygon zone, covered the R of robot that ties up among the bright noy figure _iTherefore, for convex polygon V _iCalculating, need a kind of location algorithm support distributed model.Communication range and monitoring range also as shown in Fig. 2 (b), are defined by two disks.In order to make the complete overlay area Ω of sensor network, the monitoring range of each transducer must cover its bright noy figure of dimension.

The inserted layout of Di Langnei (nested grid) is a kind of based on point set

The triangulation to flat site Ω, it is defined as: all intersect with there being one of limit two internodal any additional sides.In the drawings, direct and R _iThe node that links to each other becomes R _iAdjacent node, be defined as gathering K _iThe Di Langnei triangulation has defined the connection attribute between adjacent node, and this attribute is by boundary set ε={ e _{I_j}(t), i, j=1,2 ..., n, i ≠ j} defines.Multi-robot system is driven by connection matrix, and connection matrix A (t) is defined the connectivity of the arrangement that is used for indicating that Di Langnei is inserted.A (t) is by the uniquely defined square formation of Di Langnei triangulation.Wherein, if R _iAnd R _jBe adjacent node, A then _Ij=e _{I_j}, otherwise, A _Ij=0.For example, in Fig. 2 (a), the R of robot ₂Five adjacent nodes are arranged, and Fig. 2 (b) is the subgraph of Fig. 2 (a) Di Langnei triangulation, has specifically shown R ₂And the relation between its adjacent node.R _iBe defined as vectorial d (t)={ e with the connection attribute of its adjacent node _{I_i1}, e _{I_i2}..., e _{I_ik}} ^T, wherein, R _I1, R _I2..., R _IkBe R _iAdjacent node.e _{I_j}=e _{I_j}∠ α _{I_j}Not only defined the spacing e of two robots _{I_j}, also defined R in robot _iLocal coordinate system under, the R of robot _jDirection α _{I_j}Angle α _{I_j}Shown in Fig. 2 (c).

For a collaborative task, R ₂Move and only to be influenced by its adjacent node.Distributed robot's model description of considering adjacent node is as follows:

${\stackrel{.}{q}}_i=f_i(q_i,u_i),u_i=h_i(s_i,q_i,p_{i1},{\mathrm{<figure-callout\; id="2"\; label="p\; i"\; filenames="A20071002410000201.png"\; state="\{\{state\}\}">p</figure-callout>}}_i,\&CenterDot;\&CenterDot;\&CenterDot;,p_{\mathrm{ik}})---\left(4\right)$

Wherein, s _iBe to wait a moment the motion of introduction with reference to amount.p _I1, p _I2..., p _IkBe at the R of robot _iThe local coordinate system ∑ _iIn adjacent node R ₁, R ₂..., R _k∈ K _iThe position of  R.

In addition, one of advantage of mobile sensor network is that movable sensor can produce high-quality monitoring information and relayed information for query node by cooperation.Fig. 3 (a) has showed this mode.

Mobile robot R ₈Detect an object, and the adjacent machines people R that in the Di Langnei triangulation, defines of request ₆R ₉And R ₁₀Surround this object.By four information that transducer obtained to, query node R need sent ₁₂Merged before.Suppose that a more than transducer can monitoring object, information is transferred to a sensor node and carries out information fusion.Graph model provides a kind of perfect scheme that addresses this problem.If object in the bright noy of the dimension of sensor node zone, so this object apart from the distance of this transducer than shorter apart from other movable sensor.This node will be asked for information, and with it fusion.Data after the fusion are put the transmission path of destination node from the seedbed, obtain by the ad-hoc Routing Protocol.This invention comprises: 1) statistical model of combined sensor node is considered the sensor fusion in the network; 2) for from monitoring node to the query node transmission of Information, attribute e connects _{I_j}Statistical model; 3) the mobile sensor network of describing in order to simple form is realized and testing algorithm.

Three-dimensional mobile sensor network is than the better prospect of having of two-dimensional space.Therefore, the Cooperation And Coordination method that is distributed in three-dimensional transducer is discussed.Three-dimensional mobile sensor network comprises one group of ground dolly and a drone, or is made up of one group of ground dolly and some climbing robots.Fig. 3 (b) is a three-dimensional wireless sensor network schematic diagram.

2, sensor network kinematics analysis

In setting up in the process of mobile sensor network kinematic relation, the parameter q of robot _iIn the local coordinate system shown in Fig. 1 (b), be defined.Relation between the local coordinate system of adjacent node is to share the key of useful information between them.For each robot, local coordinate system all is different, as the R of robot among Fig. 1 (b) _iAnd R _jShown in.The sensing of each robot and data fusion are all about local coordinate system.In order to share useful information in the different machines human world, need a transformation matrix.For example, the R of robot _jDetect an object, its parameter is used the local coordinate system ∑ _jIn ξ _s ^j(t) represent.For with the R of robot _iThis information of sharing needs to determine two coordinate system ∑s _iAnd ∑ _jBetween relation.

The R of robot _iAnd R _jBetween relation can pass through transformation matrix T _i ^jAnd T _j ⁱDefine, be specifically by between two robots apart from e _{I_j}With their relative bearing θ _{I_j}Represent.Can communicate by letter between adjacent node, so that calculate relative bearing between them.Fig. 2 (c) example of having passed the imperial examinations at the provincial level.Based on the R of robot _iAnd R _jBetween communication, α _{1_2}And α _{2_1}For two robots all is as can be known.Wherein, α _{1_2}Be at R ₁R in the local coordinate system ₂The orientation; α _{2_1}Be at R ₂R in the local coordinate system ₁The orientation.The calculating formula of relative bearing is between the two: θ _{1_2}=α _{1_2}-α _{2_1}+ π.By sharing information, two robots can both the computational transformation matrix T _i ^jAnd T _j ⁱ, wherein

$T_i^j=\left[\begin{array}{cccc}\cos {\mathrm{\&theta;}}_{i\_j}& -\sin {\mathrm{\&theta;}}_{i\_j}& 0& e_{i\_j}\cos {\mathrm{\&alpha;}}_{i\_j}\\ \sin {\mathrm{\&theta;}}_{i\_j}& \cos {\mathrm{\&theta;}}_{i\_j}& 0& e_{i\_j}\sin {\mathrm{\&alpha;}}_{i\_\mathrm{<figure-callout\; id="0"\; label="j"\; filenames="A20071002410000201.png"\; state="\{\{state\}\}">j</figure-callout>}}\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]$

α _{J_i}Be at R _iR in the local coordinate system _jThe orientation.In sensor network, communication is considered to a kind of perception.Based on the location between adjacent node, resulting transformation matrix T _i ^jIt is the kinematic foundation stone of overall sensor network.In each local coordinate system, transformation matrix has defined the relation between the adjacent node, so that share Useful Information.In real system, α _{I_ j}, θ _{I_j}And e _{I_j}Be respectively to have variances sigma _{α i_j}, σ _{θ i_j}And σ _{Ei_j}Statistical variable.Thereby set up the mobile sensor network kinematic relation.

3, distributed self-organizing technique

A mobile sensor network must be a multi-robot system.In this multi-robot system, robot can cooperate to finish distributed task scheduling, such as: distributed monitoring, collaborative process.In this system, one of target of robot cooperation is: the overlay area of maximization network.Therefore, method of addition and potential field method have been introduced.In this invention, a kind of distributed self-organized algorithm based on potential field method and Di Langnei triangulation has been proposed, comprise the self-organizing of no constraint space, based on following self-organizing and the dispersion of motion planning, barrier and the environmental constraints of biology excitation, converge and fault-tolerance approach and non-holonomic constraint tracking.

In the sensor network, the coverage of each sensor node is to define according to their communication range or monitoring range, shown in Fig. 2 (b).The definition R of robot _iCommunication range be c _i, monitoring range is s _i, and c _iCompare s _iGreatly, shown in Fig. 2 (b).

For some sensor nodes, it is not closed tieing up bright noy zone, as the R among Fig. 4 ₁Can be with a special line segment

Or arc Seal this curve, as shown in Figure 4.If there is an object to appear in the zone, so Qu Yu definition will be considered the shape of object.For example: among Fig. 4, line segment With

Be used for closed area V _iThe bright noy of the dimension of gained zone V _iThe normal areas that can be considered to robot cooperated system.

In the self-organizing of no constraint space, for the mobile robot R in the multi-robot system _i, performance index (candidate's liapunov function) are defined as follows:

$v_i=\frac{1}{2}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_i{\left(\right|\left|p_{\mathrm{ij}}\right||-d_{i\_j})}^2+\frac{1}{2}{k}_{\mathrm{iv}}{\left|\right|v_i\left|\right|}^2---\left(5\right)$

Wherein, $\left|\right|p_{\mathrm{ij}}\left|\right|=\sqrt{{(x_i-x_j)}^2+{(y_i-y_j)}^2}$ And d _{I_j}Be respectively reality and the desired distance between two robots.p _IjBe at R _iIn the coordinate system, from the R of robot _iTo the R of robot _jVector.m _iBe R _iThe sum of adjacent node.k _iAnd k _IvIt is the parameter of virtual potential energy and robot dynamics's kinetic energy.The R of robot then _iControl be input as:

$u_i=-\frac{\&PartialD;V_i}{\&PartialD;p_i}-\frac{\&PartialD;V_i}{\&PartialD;v_i}=-F_i-k_{\mathrm{iv}}{v}_i,$ where $F_i=\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_i\left(\right|\left|p_{i\_j}\right||-d_{i\_j})\frac{p_{i\_j}}{\left|\right|p_{i\_j}\left|\right|}---\left(6\right)$

F _i={ F _Ix, F _IyBe by adjacent node R _iThe virtual potential field power that produces.If except R _iOther robot all is static, proves that so easily controller discussed above is global convergence.Consider that multi-robot system is an integral body, the performance index of setting up according to the potential energy of the dynamics kinetic energy of overall system and virtual potential field can be described as follows:

$V=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{V}_i=\frac{1}{2}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_i{\left(\right|\left|p_{\mathrm{ij}}\right||-d_{i\_j})}^2+\frac{1}{2}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{\left|\right|v_i\left|\right|}^2$

Based on total energy function V, can obtain the control input vector u of multi-robot system.When considering whole system, the R of robot _iVirtual potential field power be defined as:

$\frac{\&PartialD;V}{\&PartialD;p_i}=\frac{\&PartialD;V_i}{\&PartialD;p_i}+\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}\frac{\&PartialD;V_j}{\&PartialD;p_i}=\frac{\&PartialD;V_i}{\&PartialD;p_i}+\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_j\left(\right|\left|p_{j\_i}\right||-d_{j\_i})\frac{-p_{j\_i}}{\left|\right|p_{j\_i}\left|\right|}=\frac{\&PartialD;V_i}{\&PartialD;p_i}+\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_i\left(\right|\left|p_{i\_j}\right||-d_{i\_j})\frac{p_{i\_j}}{\left|\right|p_{i\_j}\left|\right|}=2\frac{\&PartialD;V_i}{\&PartialD;p_i}$

Here we suppose, k _i=k _jWhen considering whole system, R _iControl be input as:

$u_i=-\frac{\&PartialD;V}{\&PartialD;p_i}-\frac{\&PartialD;V}{\&PartialD;v_i}=-\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}2k_i\left(\right|\left|p_{i\_j}\right||-d_{i\_j})\frac{p_{i\_j}}{\left|\right|p_{i\_j}\left|\right|}+k_{\mathrm{iv}}{v}_i---\left(7\right)$

Because k _iBe controller gain, so the R of robot that is indicated by governing equation (6) and (7) _iDistributed director can think identical.Therefore, we can prove, are global convergences based on the control of the multi-robot system of virtual potential field and Di Langnei triangulation method.Based on the R of robot _iThe distributed control of adjacent node causes the whole system global convergence.

In the process of adjusting certainly, the change of Di Langnei triangulation shape is unworthy.Some nodes have increased adjacent node, and the adjacent node of some nodes has reduced simultaneously.The discontinuous variation that has caused virtual potential field power in the equation (6) of topology incident, this can cause movement locus unsmooth of node.Therefore, introduced the motion planning that encourages based on biology.

In a dynamic Di Langnei triangulation, the position of aforementioned " generator " changes in time.Up to the generation of topological incident, along with the motion of robot, the shape of triangulation is a continually varying.For a robot, topological event definition is: " generator " lost some nodes or increased some new nodes.According to the definition of virtual potential field function, for a robot, V _iContinuity depend on the quantity of its adjacent node.When it was lost or increases some adjacent nodes, violent variation had just taken place.Therefore the continuity of virtual potential field power also just has been subjected to the influence of topological incident.At first, virtual potential field power is very large, might exceed mobile robot's speed limit.In order to make movement locus level and smooth, be subjected to biological inspiration, the shunting model is introduced into.

Originally, this shunting model is based on thin-skin model development and comes.It is introduced in Control of Nonlinear Systems and the mobile robot's tracking Control.It can describe the behavior to complicated and dynamic environment real-time adaptive.In this invention, one based on virtual potential field power F _i={ F _Ix, F _Iy} ^TNew reference r _i={ r _Ix, r _Iy} ^TBe defined.New reference r _iAnd F _iThe shunting model be defined as follows:

$\frac{d{r}_{\mathrm{ix}}}{\mathrm{dt}}=-A{r}_{\mathrm{ix}}+(B-r_{\mathrm{ix}})h_{+}\left(F_{\mathrm{ix}}\right)-(D+r_{\mathrm{ix}})h_{-}\left(F_{\mathrm{ix}}\right)$

$\frac{d{r}_{\mathrm{iy}}}{\mathrm{dt}}=-A{r}_{\mathrm{iy}}+(B-r_{\mathrm{iy}})h_{+}\left(F_{\mathrm{iy}}\right)-(D+r_{\mathrm{iy}})h_{-}\left(F_{\mathrm{iy}}\right)---\left(8\right)$

Parameter A, B and D are respectively the upper and lower bounds of passive attenuation rate, nervous system behavior.Function h ₊() and h _-() represented the excitation input respectively and suppressed input, and they are defined as: h ₊(x)=and max (0, x), h _-(x)=max (0 ,-x).For any excitation and inhibition input F _i, the new virtual potential field power based on the shunting model all has been limited among bounded interval-[D, the B].Based on this new potential field power, the control law of equation (6) representative is changed to:

u _i＝-r _i-k _ivv _i (9)

In addition, carrying out the distributed of network based on particle group optimizing method can be described as from deployment and self-organizing:

The self-organizing of network arranges that the optimization problem that needs to solve is: cluster is at the transducer of certain regional random distribution, how to control them in the short period of time, carry out self-organizing and arrange, make the coverage maximization of sensor network, and each transducer reaches even distribution.

Also have a constraints in addition, that is: guarantee after network carries out self-organizing that each node has at least one one hop node to be communicated with it, guarantees internodal communication.

Following specific description adopts particle cluster algorithm to carry out the process of self-organization of network:

When node more after a little while, for the cluster transducer, adopt following method to carry out the self-organizing of node.

1. to the hypothesis of particulate: owing to be self-organizing at the enterprising line sensor network of two dimensional surface, so establish x _i=(x _I1, x _I2..., x _Im), y _i=(y _I1, y _I2..., y _Im) i=1,2 ..., n is the position vector of i particulate among the particulate group.Wherein, m represents the quantity of node in the cluster transducer; N represents particulate group's scale, that is: n particulate arranged among the particulate group; x _iAnd y _iAbscissa and the ordinate of representing i particulate position respectively.

Establish v again _Xi=(v _Xi1, v _Xi2..., v _Xim) v _Yi=(v _Yi1, v _Yi2..., v _Yim) be respectively that particulate i is along the velocity vector on x and the y direction; p _Xi=(p _Xi1, p _Xi2..., p _Xim), p _Yi=(p _Yi1, p _Yi2..., p _Yim) be particulate i in optimizing process the horizontal ordinate of position of process with best adaptive value; p _Xg=(p _Xg1, p _Xg2..., p _Xgm), p _Yg=(p _Yg1, p _Yg2..., p _Ygm) be the horizontal ordinate of the optimal location that searches of whole population.Wherein, the implication of m, n is with top identical.

2. the initialization of particulate: arrange because node generally carries out self-organizing around bunch head.Again because the needs of inter-node communication, so, when the particulate initialization, being the center of circle, be node initializing in the circle of radius with 1 with bunch head.Such initialization, originally the transducer in the expression self-organizing network is to center on a bunch random arrangement.

3. the calculating of fitness: establish each euclidean distance between node pair and be:

In the self-organization of network model of being discussed, the cluster transducer carries out the self-organizing configuration in certain zone, be to lead this bunch mobile node to enter this zone by a bunch headband earlier, in this zone, carry out the self-organizing configuration around bunch head then, so be positioned at the center of network as bunch first, other transducer is as its hop node.Therefore, the employed fitness function of this paper is:

Wherein, d _IjBe the distance between node i and the node j, D _kBe k node except that bunch head to the distance of bunch head, the implication of m is still the same.Through after changing, fitness function is represented be the node distance that arrives other node except that bunch head with, get fitness function like this, reduced optimization aim, can make the self-organizing of network rapider.

4. the improvement evolution equation of speed and position:

For the j of i particulate dimension (j transducer in the cluster transducer), evolve to the position in t+1 generation and speed with following evolution equation calculating from t generation:

x _ij(t+1)＝x _ij(t)+η(t)v _xij(t+1)

y _ij(t+1)＝y _ij(t)+η(t)v _yij(t+1) (10)

v _xij(t+1)＝ω(t)v _xij(t)+c ₁r ₁(p _xij(t)-x _ij(t))+c ₂r ₂(p _xgi(t)-x _ij(t))

v _yij(t+1)＝ω(t)v _yij(t)+c ₁r ₁(p _yij(t)-y _ij(t))+c ₂r ₂(p _ygi(t)-y _ij(t)) (11)

Wherein, i=1,2 ..., n, j=1,2 ..., m, ω are inertia weight, c ₁, c ₂Be acceleration constant, r ₁, r ₂Be equally distributed random number in [0,1].ω, c ₂With η is dynamically to adjust, and its adjustment law is respectively:

ω(t)＝ω _max-(ω _min×t/g _max) (12)

$\mathrm{\&eta;}\left(t\right)=\frac{({\mathrm{\&eta;}}_{\max }-{\mathrm{\&eta;}}_{\min })}{1+\exp [\mathrm{\&lambda;}\&times;(t-g_{\max }/2)]}+{\mathrm{\&eta;}}_{\min }---\left(13\right)$

$c_2=\frac{({\mathrm{<figure-callout\; id="2"\; label="c"\; filenames="A20071002410000201.png"\; state="\{\{state\}\}">c</figure-callout>}}_{2\max }-{\mathrm{<figure-callout\; id="2"\; label="c"\; filenames="A20071002410000201.png"\; state="\{\{state\}\}">c</figure-callout>}}_{2\min })}{1+\exp [(-\mathrm{\&lambda;})\&times;(t-g_{\max }/2)]}+{\mathrm{<figure-callout\; id="2"\; label="c"\; filenames="A20071002410000201.png"\; state="\{\{state\}\}">c</figure-callout>}}_{2\min }---\left(14\right)$

In the formula, ω _Max, ω _MinBe maximum and the minimum value that ω changes; η _Max, η _MinBe maximum and the minimum value that η changes, generally get η _Max∈ [1.0,1.8], η _Min∈ [0.4,0.8]; c _2max, c _2minBe c ₂The maximum and the minimum value that change are generally got c _2max∈ [1.6,2.0], c _2min∈ [0.6,1.0]; T is an optimizing algebraically, g _MaxBe maximum iteration time; λ is a constant, generally gets λ ∈ [0.005,0.015].

5. the processing of constraints: after passing through the calculating of Position And Velocity evolution equation, the position of node may surpass the communication range of bunch head in the sensor network, and tackle it and reinitialize according to the initialized method in front this moment.

Utilize above-mentioned self organizing network model, and, guaranteeing under the prerequisite that to communicate by letter between node, must make the coverage maximization of network, and each transducer can evenly distribute the self-organizing of realization sensor network in conjunction with particle cluster algorithm.

When considering than the self-organizing problem of multinode in certain zone, if fitness function also according to the internodal distance that adopts above and, increase along with node, internodal connection meeting is with exponential increase, the target of optimizing also can increase, and algorithm may cause self-organization of network not prompt enough.At this moment need introduce auxilliary bunch head.

Auxilliary bunch head is to carry out some nodes after the self-organizing according to the method in the last trifle.It is intended for new center, instructs other transducer that does not dispose to carry out self-organizing.Concrete grammar is as follows.

At first, use said method, count N, calculate the main bunch head optimal location of node on every side according to given node around main bunch head.And all the sensors is divided into N submanifold of quantity approximate equality;

Secondly, in N submanifold, select a node respectively as auxilliary bunch head, it is configured to the optimal location place of being calculated by main bunch head, in this course, the submanifold at its place is followed it and is moved near the optimal location;

Once more, each auxilliary bunch head calculates their optimal locations (will consider that wherein the node that has configured is no longer to move) on every side again; And according to the optimal location sensors configured;

So carry out recursion, make whole network constantly launch, finish up to all the sensors self-organizing.

To the self-organizing under barrier and the environmental constraints, can be based on the Di Langnei triangulation of virtual potential field method with environmental constraints and dynamic barrier combined factors.For environment as shown in Figure 5, based on the heat transfer agent of robot, additional connection is added into.Additional connection is used to define the R of robot _iVirtual energy function.Have the energy of the system of barrier to be described as:

$V_i=\frac{1}{2}\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_{\mathrm{ij}}{\left(\right|\left|p_{\mathrm{ij}}\right||-d_{i\_j})}^2+\frac{1}{2}\underset{l=1}{\overset{m_o}{\mathrm{\&Sigma;}}}{k}_{\mathrm{il}}{\left(\right|\left|p_{i\_l}\right||-d_{i\_l})}^2$

Wherein, k _IlBe a parameter, m _oBe the R of robot _iThe total quantity of barrier in the monitoring range, ‖ p _{I_l}‖ and d _{I_l}Be respectively actual range and the desired distance between robot and barrier.The R of robot _iOnly consider its adjacent node and the barrier in its monitoring range.

Subsequently, we can access node R _iVirtual potential field power:

$F_i=\frac{\&PartialD;V_i}{\&PartialD;p_i}=\underset{j=1}{\overset{m_i}{\mathrm{\&Sigma;}}}{k}_{\mathrm{ij}}\left(\right|\left|p_{i\_j}\right||-d_{i\_j})\frac{p_{i\_j}}{\left|\right|p_{i\_j}\left|\right|}+\underset{j=1}{\overset{m_o}{\mathrm{\&Sigma;}}}{k}_{\mathrm{il}}\left(\right|\left|p_{i\_l}\right||-d_{i\_l})\frac{p_{i\_l}}{\left|\right|p_{i\_l}\left|\right|}---\left(15\right)$

Based on new potential field power, the robot controller under the environment that is tied can be devised.

For distributed mobile sensor network, disperseing and converging is two important operations.With the controller that equation (6) and equation (9) are described, can be by adjusting the desired distance d between two nodes _{I_j}Solve the problem of disperseing and converging.If the mobile robot is that algorithm can make that robot converges, so that cover certain zone in the sparse space that is configured in an opening.

For a multi-robot system, one of most important characteristic is its redundancy and fault-tolerance.

If one or more robots had lost efficacy, other robot can reconfigure to their position according to the needs of task.

For the multi-robot system of forming by the non-holonomic constraint mobile robot, have to revise above-mentioned algorithm, so that satisfy the non-holonomic constraint of robot.Based on the transducer of having equipped tracking control unit,, can realize by the combination of Di Langnei triangulation and virtual potential field method to non-holonomic constraint mobile robot's tracking Control.For a non-holonomic constraint robot shown in Fig. 1 (b), along the X of robot _iThe a bit of reference Point C apart from d that has of ' axle is specified.The center and the kinematic relation between the C of trailing wheel can be described below: ${\stackrel{.}{x}}_{\mathrm{ir}}=v_i\cos \mathrm{\&theta;}-d\&CenterDot;w_i\sin \mathrm{\&theta;},$ ${\stackrel{.}{y}}_{\mathrm{ir}}=v_i\sin \mathrm{\&theta;}+d\&CenterDot;w_i\cos \mathrm{\&theta;}.$ Imperfect ROBOT CONTROL input based on virtual potential field power is described as: $v_i=(r_{\mathrm{ix}}\cos \mathrm{\&theta;}+r_{\mathrm{iy}}\sin \mathrm{\&theta;})+({\stackrel{.}{x}}_i^d\cos \mathrm{\&theta;}+{\stackrel{.}{y}}_i^d\sin \mathrm{\&theta;}),$ $w_i=\frac{1}{d}[(r_{\mathrm{iy}}\cos \mathrm{\&theta;}-r_{\mathrm{ix}}\sin \mathrm{\&theta;})+({-\stackrel{.}{x}}_i^d\sin \mathrm{\&theta;}+{\stackrel{.}{y}}_i^d\cos \mathrm{\&theta;})].$ Virtual potential field power r _iX along robot _i' axle and Y _i' axle,

Be broken down on two vertical directions, shown in Fig. 1 (b).A generation is along X _iThe lateral displacement of ' axle, another produces angular displacement.In design, along X _iForward direction that ' direction allowed and back are unnecessary to moving.

4, the formation control of sensor node and cooperation technology

The target of controlling of forming into columns is to coordinate one group of robot with a kind of method.This method is: with respect to other robot, they will keep a kind of given formation.One group of robot can be used for carrying out a task very difficult for a robot.For search and rescue under the hazardous environment and military aspect, formation is important, can cooperate and finish the task of a complexity.Do not consider the robot diversified application background of forming into columns, the target of multi-robot coordination algorithm is to accomplish a task with being used for cooperation.In this invention, we propose a kind of distributed algorithm of formation control exploitation for large scale system.Distributed director is based on model q _i=f _i(q _i, u _i), u _i=h _i(s _i, q _i, p _I1, p _I2..., p _Ik).Wherein, s _iBe called motion reference, it is the task of forming into columns.Each mobile robot's controller all is the linear combination of task object and its local virtual potential field power.This invention has related to the method for two kinds of controls of forming into columns---the asynchronous formation control and the control of forming into columns synchronously.And, composite formation's control method has been proposed based on this.

In robot formed into columns, iff the desired motion that is certain the robot understand network in the sensor network, and it did not inform other robot by communication, and we claim that this situation is asynchronous formation control so.There is the robot of whole formation desired motion information to be called as in " leader robot ".If " leader robot " according to the position of the exercise program change of forming into columns oneself, its adjacent machines people also can this motion of perception by the change of relative distance between them so.

Although the form of forming into columns in asynchronous formation control changes sometimes slightly, the robot in the sensor network can both follow the motion of " leader robot ".Under the local coordinate system of each robot, all motions of controlling them are unworthy.For global coordinate system less than the approval.In asynchronous formation system, the motion of " leader robot " is to be subjected to the restricted number of robot in the response time of monitoring system and the sensor network.If the speed of " leader robot " is too big, other robot may not catch up with its motion, and formation can be damaged.

The control of forming into columns synchronously is such: sensor network is the network of a synchronous communication, if a robot receives the formation moving projection, the network agreement that floods just is used to give other robot in the network formation planned transmission.For different transducers, identical plan is explained it is unnecessary respectively.S is expressed as the expected path of formation, then s _iBe that it is at the R of robot _iExplanation under the coordinate system.Adjacent node R _iAnd R _jBetween the shared of information be by transformation matrix T _i ^jFinish.

The control of forming into columns synchronously is not a kind of " leader---follower " system.If " leader robot " lost efficacy, other any one robot can both replace it as " leader robot ".

For model u _i=h _i(s _i, q _i, p _I1, p _I2..., p _Ik), if s _iConflict with virtual potential field power, virtual potential field power can be arranged motion, and this causes moving forward and backward of robot sometimes.In order to reduce the consumption of energy, designed a kind of hybrid formation control.If R _iThe motion and the plan s of formation inconsistent, another controller u so _iWill be called.Wherein the motion of each individual robot all is consistent with the exercise program of formation.Hybrid algorithm needs sensor node that the consistent of exercise program admitted.The purpose of composite formation's control algolithm is improve network intelligent.Composite formation's control algolithm of more complexity is designed.For example, when other robot was freely disposed, three adjacent machines people can form a kind of special geometric figure in the network.

Distributed formation control algolithm is a kind of expansion from setting algorithm.The utilization of formation control algolithm is admitted the consistent of task object s, and each mobile robot explains it according to own position in sensor network.The only conversion of existence formation has just been formed into columns by robot like this.Therefore, we propose mobile sensor network formed into columns and are defined as " visual human ", and this just means that formation can conversion and rotation in two-dimensional space so, forms into columns and also can disperse and converge.Combining, develop a kind of distributed algorithm and come " visual human " operated to the consistent algorithm of admitting of task object with from setting algorithm.Perhaps develop a kind of composite formation control algolithm.In mobile sensor network, the control of adjusting certainly and form into columns is important, but for the monitoring task, the cooperation of movable sensor is more importantly.Hybrid algorithm can be considered the demand and the overlay area of transducer cooperation simultaneously, and realizes the merging of forming into columns, the interception of barrier etc.

5, mobile robot ad-hoc route and Data Transport Protocol technology

The cooperation of multi-robot system is supported by the ad-hoc communication network.Monitoring Data is propagated in network also needs to pass through communication network.For the data query of suitable operation and mobile sensor network, be essential to the identification of network and control.In the mobile sensor network, each mobile robot is configured to router.For transmission of monitoring data and packet in network, to the improvement of required ad-hoc Routing Protocol.The transmission of data is different from general ad-hoc network in the mobile sensor network.For example: shown in Fig. 3 (a), for from source node R ₈To destination node R ₁₂The transmission of monitoring data need to seek a paths.In a router, the Monitoring Data of input need be transformed under the local coordinate system, has the required local coordinate information of next router and binds with the output Monitoring Data again.The Routing Protocol that is proposed be based on distributed Di Lang inner model and sensor network kinematic.

Based on the sensor network kinematics, the relative position of two adjacent nodes is very important as can be seen.The MAC layer and the application layer of wireless network have been proposed locating information is integrated into.As prototype, the existing IEEE802.11MAC layer of our plan modification is so that make it comprise locating information.This method can overcome the problem of hiding in the mobile ad-hoc wireless network with exposed node.Each node of network all has locating information, and when it attempts to obtain the access right of common share communication medium (CTS/RTS exchanges data), propagates this locating information.Node listens to this information, and calculates about the relative position between transmitter and receiver.From locating information, other node of wishing communication can both know its emission whether can disturb right in nodes in communication.If node determines that it is enough far away apart from other communication node, it just begins emission so.Because know all that about the locating information that all existing communications connect the energy of adjusting transmitter so dynamically is also just easy, this adjustment helps making unnecessary interference to drop to minimum.This will cause: 1) because more node can use communication media, so increased the message capacity of channel; 2) because reduced power consumption, so prolonged the life cycle of node.

Claims (9)

1. independent mobile sensor network dynamic modeling and control technology is characterized in that being,

Step 1: set up distributed mobile sensor network model based on local Di Langnei triangulation and Wei Lang noy figure, wherein the Di Langnei triangulation is described the transducer annexation with the triangle geometry, tie up the monitoring range that bright noy figure is used for describing sensor node, on the model basis that this combines, set up the static and dynamic performance of expressing the whole sensor network;

Step 2: utilize two kinds of methods to carry out the distributed from disposing and self-organizing of network to the control sensor node under arbitrary zone in this distributed mobile sensor network model, these two kinds of methods are respectively: based on the distributed self-organizing method of virtual potential field method and Di Langnei triangulation; Distributed self-organizing method based on the modified model particle group optimizing method;

Step 3: adopt formation algorithm controls network node to form into columns and the node cooperation, reach abundant purpose of energy saving;

Step 4: set up distributed mobile robot's self-organizing network ad-hoc route data host-host protocol, realize the steady operation of whole independent mobile sensor network.

2. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology is characterized in that: after step 1, before the step 2, carry out the basic exercise credit based on this model and analyse;

When analyzing, set up the dynamic model of each mobile robot's subsystem respectively according to each mobile robot's position, deflection, speed and controlled quentity controlled variable information; According to the dynamic model of each subsystem of setting up and the relation between integrated subsystem and the adjacent node, set up distributed model again based on graph theory; Then, based on the distributed model of being built, carry out information gathering, fusion.

3. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology is characterized in that: based on virtual potential field method and the distributed self-organizing method Di Langnei triangulation, make transducer carry out self-organizing in no constraint space;

The basic thought of virtual potential field method is: the motion of sensor node in environment is considered as a kind of virtual motion that manually is subjected in the field of force; Barrier produces repulsion to robot, and impact point produces gravitation, the making a concerted effort as the accelerative force of sensor node, the movement velocity of Control Node and direction of repulsion and gravitation;

When self-organizing, derive its control law by the performance index function of a robot; When considering whole multi-robot system, derive the group control rule; Foundation is based on the self-organizing method of no constraint space in the distributed self-organizing method of virtual potential field method and Di Langnei triangulation.

4. independent mobile sensor network dynamic modeling as claimed in claim 3 and control technology is characterized in that: on the basis of the no constraint space self-organizing of transducer, add the motion planning of the biological excitation of simulation;

The basic thought of the motion planning of the biological excitation of simulation is: the simulated nervous system behavior, virtual potential field power is limited in the bounded interval, and make the comparatively continuous variation of virtual potential field power, and then make that the movement locus of sensor node is comparatively level and smooth.

5. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology, it is characterized in that: the distributed self-organizing method based on the modified model particle group optimizing method mainly is to utilize particle cluster algorithm, and, calculate the follow-up location of other node according to the positional information of known sensor node; The sensor node of network is divided into main bunch head, auxilliary bunch head and ordinary node, and wherein, a main bunch head plays a part to drive and instruct whole network node, and auxilliary bunch head plays a part to drive and instruct the node in certain zone, and remaining node is an ordinary node; By the drive and the directive function of main and auxiliary bunch of head, carry out the distributed of network from disposing and self-organizing; Arrive assigned address by main and auxiliary bunch of headband neck network, and guide network to launch gradually.

6. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology is characterized in that: when carrying out the self-organizing of transducer under barrier and environmental constraints are arranged, derive its control law according to the energy function of the system that barrier is arranged; Foundation is based on the transducer self-organizing method under barrier and environmental constraints in the distributed self-organizing method of virtual potential field method and Di Langnei triangulation.

7. as claim 1 or 3 or 5 described independent mobile sensor network dynamic modeling and control technologys, it is characterized in that: with the distributed self-organizing method of transducer be applied in the dispersion that comprises node, converge with fault-tolerant task in, solve and disperse and converge problem by adjusting desired distance between two nodes.

8. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology is characterized in that: distributed self-organizing method is used to solve the tracking Control problem of the movable sensor with non-holonomic constraint.

9. independent mobile sensor network dynamic modeling as claimed in claim 1 and control technology is characterized in that: described formation algorithm comprises asynchronous formation algorithm, form into columns algorithm and composite formation's algorithm synchronously; Constituting by synchronous formation algorithm and asynchronous formation algorithm wherein by composite formation's algorithm.

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